Green infrastructure planning in Vancouver : addressing environmental justice with participatory resident workshops
Bibliographic record
Abstract
Urban rain gardens, wetlands, street trees: these “green infrastructures” (GI) are being used for a variety of urban planning priorities, like climate adaptation and rainwater management. Environmental justice scholars have stressed the need to develop green infrastructure for those who need it most (Anguelovski et al., 2021; Meerow & Newell, 2019). They have also identified “blind spots” in planning processes—from siting, through to public engagement, to maintenance—that may perpetuate uneven development or power imbalances (Brent et al., 2022; Zuniga-Teran et al., 2020). In Vancouver (Canada), modeling and mapping exercises have identified areas that can benefit most from GI development (“equity initiative zones” (City of Vancouver, 2022c); “areas in need of resources” (City of Vancouver, 2022b). This analysis helpfully indicates who is experiencing environmental vulnerability (e.g., heat, sea level rise), socio-economic vulnerability (e.g., low-income), and lack of urban green amenities (e.g., park access) in the city. As scholars recommend, however, there is a need to understand what these overlapping experiences mean to affected residents, and perhaps more importantly, what residents see as appropriate environmental and climate planning priorities as a result (Hoover et al., 2021). In this project, I facilitated two participatory workshops with residents who live in Vancouver’s eastern neighbourhoods, asking: what are residents’ self-identified GI priorities, challenges, and aspirations? Participants shared how GI projects can be adapted to meet their needs as renters, parents, seniors, immigrants, and low-income individuals. Participants wanted to see GI in the everyday spaces where they spend their time, noting possibilities such as developing green roofs directly on their affordable housing units. Second, participants stressed that improved livability (namely through public transit and affordable housing) can improve their overall experience with GI. Supplementary expert interviews (n=4) and an integrative document review (n=25) revealed other factors that might obscure or limit pathways for equitable development. These factors include opportunistic development patterns, budgetary constraints, and a lack of specific, actionable equity objectives. As the City of Vancouver continues to strive for equitable green infrastructure development, this project synthesizes potential entry points and limitations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".